Open AccessReservoir Performance Prediction using Integrated Production Modelling (MBAL Software)
Author(s) -
Prathamesh Sapale,
Vishesh Bhadariya,
Ashok Kumar Aachari N.,
Nitesh Paliwal,
Shreeharsha Vandavasi
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d7627.118419
Subject(s) - material balance , reservoir engineering , reservoir simulation , petroleum engineering , matching (statistics) , computer science , production (economics) , oil field , software , performance prediction , field (mathematics) , set (abstract data type) , reservoir modeling , flow (mathematics) , industrial engineering , petroleum reservoir , geology , petroleum , engineering , simulation , process engineering , statistics , paleontology , geometry , mathematics , pure mathematics , economics , macroeconomics , programming language
Reservoir performance prediction is important aspect of the oil & gas field development planning and reserves estimation which depicts the behavior of the reservoir in the future. Reservoir production success is dependent on precise illustration of reservoir rock properties, reservoir fluid properties, rock-fluid properties and reservoir flow performance. Petroleum engineers must have sound knowledge of the reservoir attributes, production operation optimization and more significant, to develop an analytical model that will adequately describe the physical processes which take place in the reservoir. Reservoir performance prediction based on material balance equation which is described by Several Authors such as Muskat, Craft and Hawkins, Tarner’s, Havlena & odeh, Tracy’s and Schilthuis. This paper compares estimation of reserve using dynamic simulation in MBAL software and predictive material balance method after history matching of both of this model. Results from this paper shows functionality of MBAL in terms of history matching and performance prediction. This paper objective is to set up the basic reservoir model, various models and algorithms for each technique are presented and validated with the case studies. Field data collected related to PVT analysis, Production and well data for quality check based on determining inconsistencies between data and physical reality with the help of correlations. Further this paper shows history matching to match original oil in place and aquifer size. In the end conclusion obtained from different plots between various parameters reflect the result in history match data, simulation result and Future performance of the reservoir system and observation of these results represent similar simulation and future prediction plots result.